Classifying multi-level stress responses from brain cortical EEG in Nurses and Non-health professionals using Machine Learning Auto Encoder

Akella, A; Singh, AK; Leong, D; Lal, S; Newton, P; Clifton-Bligh, R; McLachlan, CS; Gustin, SM; Maharaj, S; Lees, T; Cao, Z; Lin, C-T

Classifying multi-level stress responses from brain cortical EEG in Nurses and Non-health professionals using Machine Learning Auto Encoder

Akella, A

Singh, AK Leong, D Lal, S

Newton, P Clifton-Bligh, R McLachlan, CS Gustin, SM Maharaj, S Lees, T Cao, Z

Lin, C-T

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Journal of Translational Engineering in Health and Medicine, 2021, 9, pp. 2200109
Issue Date:: 2021-01-01

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Field	Value	Language
dc.contributor.author	Akella, A https://orcid.org/0000-0002-8719-5466
dc.contributor.author	Singh, AK
dc.contributor.author	Leong, D
dc.contributor.author	Lal, S https://orcid.org/0000-0002-0911-0850
dc.contributor.author	Newton, P
dc.contributor.author	Clifton-Bligh, R
dc.contributor.author	McLachlan, CS
dc.contributor.author	Gustin, SM
dc.contributor.author	Maharaj, S
dc.contributor.author	Lees, T
dc.contributor.author	Cao, Z https://orcid.org/0000-0003-3656-0328
dc.contributor.author	Lin, C-T
dc.date.accessioned	2021-09-22T00:11:09Z
dc.date.available	2021-04-09
dc.date.available	2021-09-22T00:11:09Z
dc.date.issued	2021-01-01
dc.identifier.citation	IEEE Journal of Translational Engineering in Health and Medicine, 2021, 9, pp. 2200109
dc.identifier.issn	2168-2372
dc.identifier.issn	2168-2372
dc.identifier.uri	http://hdl.handle.net/10453/150654
dc.description.abstract	<h4>Objective</h4>Mental stress is a major problem in our society and has become an area of interest for many psychiatric researchers. One primary research focus area is the identification of bio-markers that not only identify stress but also predict the conditions (or tasks) that cause stress. Electroencephalograms (EEGs) have been used for a long time to study and identify bio-markers. While these bio-markers have successfully predicted stress in EEG studies for binary conditions, their performance is suboptimal for multiple conditions of stress.<h4>Methods</h4>To overcome this challenge, we propose using latent based representations of the bio-markers, which have been shown to significantly improve EEG performance compared to traditional bio-markers alone. We evaluated three commonly used EEG based bio-markers for stress, the brain load index (BLI), the spectral power values of EEG frequency bands (alpha, beta and theta), and the relative gamma (RG), with their respective latent representations using four commonly used classifiers.<h4>Results</h4>The results show that spectral power value based bio-markers had a high performance with an accuracy of 83%, while the respective latent representations had an accuracy of 91%.
dc.format	Electronic-eCollection
dc.language	eng
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Journal of Translational Engineering in Health and Medicine
dc.relation.isbasedon	10.1109/jtehm.2021.3077760
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Classifying multi-level stress responses from brain cortical EEG in Nurses and Non-health professionals using Machine Learning Auto Encoder
dc.type	Journal Article
utslib.citation.volume	9
utslib.location.activity	United States
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Life Sciences
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Centre for Health Technologies (CHT)
utslib.copyright.status	open_access	*
dc.date.updated	2021-09-22T00:11:06Z
pubs.publication-status	Published
pubs.volume	9

Abstract:

Objective

Mental stress is a major problem in our society and has become an area of interest for many psychiatric researchers. One primary research focus area is the identification of bio-markers that not only identify stress but also predict the conditions (or tasks) that cause stress. Electroencephalograms (EEGs) have been used for a long time to study and identify bio-markers. While these bio-markers have successfully predicted stress in EEG studies for binary conditions, their performance is suboptimal for multiple conditions of stress.

Methods

To overcome this challenge, we propose using latent based representations of the bio-markers, which have been shown to significantly improve EEG performance compared to traditional bio-markers alone. We evaluated three commonly used EEG based bio-markers for stress, the brain load index (BLI), the spectral power values of EEG frequency bands (alpha, beta and theta), and the relative gamma (RG), with their respective latent representations using four commonly used classifiers.

Results

The results show that spectral power value based bio-markers had a high performance with an accuracy of 83%, while the respective latent representations had an accuracy of 91%.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/150654